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# `WorkerMetricsState` helpers
## Purpose
`abstract-metrics.ts` carries the pure functions and the small accumulator used by `AbstractWorker` to track success/failure counts and average response time. Splitting them out of `abstract.ts` keeps each file under the 300-LOC budget, makes the bookkeeping unit-testable in isolation, and lets a subclass reuse the same helpers if it overrides `getHealth` with additional fields layered on top of the base payload.
## Features
- `createMetricsState()` — fresh `WorkerMetricsState` (`tasksSucceeded`, `tasksFailed`, `totalResponseTime`, `totalTasksCompleted`).
- `recordSuccess(state, responseTimeMs)` — increment successes, accumulate response time.
- `recordFailure(state)` — increment failures only (response time is undefined for failures).
- `buildDefaultHealth(state, { activeTasks, isHealthy })` — synthesise the default `WorkerHealth` payload using the accumulator's totals.
- CPU / memory in the synthesised payload scale linearly with `activeTasks` and cap at 100 — purely synthetic, intended as a placeholder until subclasses inject real measurements.
## Usage: Basic Example
```ts
import {
createMetricsState,
recordSuccess,
recordFailure,
buildDefaultHealth,
} from "@suaveplan/queue-distribution/worker/abstract/abstract-metrics";
const state = createMetricsState();
recordSuccess(state, 120);
recordSuccess(state, 80);
recordFailure(state);
const health = buildDefaultHealth(state, { activeTasks: 1, isHealthy: true });
// health.metrics.responseTime === 100
// health.metrics.tasksSucceeded === 2
// health.metrics.tasksFailed === 1
```
## Usage: Advanced Example
A subclass can compose its own metrics on top of the default payload — for example, a remote worker that wants to pass through CPU / memory from the upstream server while keeping the synthesised counters.
```ts
import { AbstractWorker } from "@suaveplan/queue-distribution/worker/abstract/abstract";
import { buildDefaultHealth } from "@suaveplan/queue-distribution/worker/abstract/abstract-metrics";
import type { WorkerHealth } from "@suaveplan/queue-distribution/worker/worker";
class RemoteShim extends AbstractWorker {
override async getHealth(): Promise<WorkerHealth> {
const base = buildDefaultHealth(
(this as unknown as { metrics: ReturnType<typeof createMetricsState> }).metrics,
{ activeTasks: this.activeTasks, isHealthy: this.status !== "OFFLINE" },
);
const upstream = await fetchUpstreamHealth();
return {
isHealthy: base.isHealthy && upstream.ok,
metrics: { ...base.metrics, cpu: upstream.cpu, memory: upstream.memory },
};
}
protected async doExecuteTask<I, O>(t, i): Promise<O> {
return undefined as never;
}
protected getType(): string {
return "shim";
}
}
```
## API Reference
- `interface WorkerMetricsState` — `tasksSucceeded`, `tasksFailed`, `totalResponseTime`, `totalTasksCompleted` (all numbers).
- `function createMetricsState(): WorkerMetricsState`.
- `function recordSuccess(state, responseTimeMs): void`.
- `function recordFailure(state): void`.
- `function buildDefaultHealth(state, { activeTasks, isHealthy }): WorkerHealth`.
## Implementation Notes
The CPU / memory placeholders (`activeTasks * 25`, `activeTasks * 15`, capped at 100) are explicitly synthetic — the upstream library returned the same shape and downstream consumers may have come to expect a non-zero CPU value when the worker is doing anything. Subclasses that have access to real OS-level metrics should override `getHealth()` end-to-end rather than calling `buildDefaultHealth` first; the helper exists so subclasses can layer on top without losing the success/failure counters.

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# `AbstractWorker`
<!-- begin:auto-docs -->
_(no submodules)_
<!-- end:auto-docs -->
## Purpose
`AbstractWorker` is the base class every concrete worker (in-process, remote, worker-thread) extends. It owns lifecycle bookkeeping (`start` / `stop`), metric accumulation (success / failure counts, response-time totals), OTel span emission per task, status transitions between `ONLINE` and `BUSY`, and an injectable `Clock` / `IdGenerator` / `LoggerLike` so subclasses focus exclusively on transport-specific behaviour. Splitting the bookkeeping out of every transport means `LocalWorker`, `RemoteWorker`, and `WorkerThreadWorker` need only override `doExecuteTask`, `doStart`, and `doStop`.
## Features
- Idempotent `start()` (no-op when not `OFFLINE`).
- `stop(force)` rejects when `activeTasks > 0 && !force` with `WORKER_BUSY_STOP`; force-stop drains active count.
- `executeTask` increments `activeTasks`, transitions status to `BUSY` when going 0→1, records duration via injected `Clock`, emits OTel histograms/counters/spans, decrements on completion, restores `ONLINE` when count returns to zero.
- `markUnhealthy(reason)` flips status to `UNHEALTHY` and warns through the logger.
- `getHealth()` synthesises a default payload from accumulated metrics; subclasses override for real probes.
- `getInfo()` builds a structural `WorkerInfo` snapshot for pool introspection.
- Injectable `Clock`, `IdGenerator`, `LoggerLike`; defaults are `Date.now`, `crypto.randomUUID`, and `NOOP_LOGGER`.
## Usage: Basic Example
```ts
import { AbstractWorker } from "@suaveplan/queue-distribution/worker/abstract/abstract";
import type { DistributedTask } from "@suaveplan/queue-distribution/task/task";
class HelloWorker extends AbstractWorker {
constructor() {
super({ capacity: 4, weight: 1 });
}
protected async doExecuteTask<I, O>(
task: DistributedTask<I, O>,
input: I,
): Promise<O> {
return task.execute(input, {});
}
protected getType(): string {
return "hello";
}
}
```
## Usage: Advanced Example
Inject a deterministic `Clock` and `IdGenerator` for property-based tests. Override `doStart` / `doStop` for any transport that needs connection setup or graceful teardown.
```ts
import { AbstractWorker } from "@suaveplan/queue-distribution/worker/abstract/abstract";
import type { DistributedTask } from "@suaveplan/queue-distribution/task/task";
class ConnectedWorker extends AbstractWorker {
constructor(private readonly endpoint: string) {
super(
{ capacity: 2, weight: 1 },
{
clock: () => 1_700_000_000_000,
idGenerator: () => `worker-${endpoint}`,
logger: console as never,
},
);
}
protected async doStart(): Promise<void> {
// open connection here
}
protected async doStop(force: boolean): Promise<void> {
// tear connection down here; honour `force`
}
protected async doExecuteTask<I, O>(
task: DistributedTask<I, O>,
input: I,
): Promise<O> {
return task.execute(input, { endpoint: this.endpoint });
}
protected getType(): string {
return "connected";
}
}
```
## API Reference
- `abstract class AbstractWorker implements Worker`.
- `constructor(options?: AbstractWorkerOptions, deps?: AbstractWorkerDeps)`.
- `AbstractWorkerOptions = { id?, capacity?, weight? }`.
- `AbstractWorkerDeps = { clock?, idGenerator?, logger? }`.
- `executeTask<I, O>(task, input, signal?): Promise<O>`.
- `getHealth(signal?): Promise<WorkerHealth>` — synthesised default; subclasses may override.
- `start(signal?): Promise<void>`, `stop(force?, signal?): Promise<void>`.
- `canAcceptTask(): boolean`, `getInfo(): WorkerInfo`.
- `markUnhealthy(reason): void`.
- Abstract: `protected doExecuteTask<I, O>(task, input): Promise<O>`, `protected getType(): string`.
- Hookable: `protected doStart(signal?)`, `protected doStop(force, signal?)`, `protected getCapabilities()`, `protected getName()`.
## Implementation Notes
The hot path inside `#runWithBookkeeping` always restores `activeTasks` via `finally`, even on synchronous throws inside `doExecuteTask`, so the worker can never leak a phantom active-task slot. The OTel emission uses a single tracer reference cached in `observability/instruments.ts` so emitter setup doesn't allocate per task. Defaulting `idGenerator` to `crypto.randomUUID` removes the `uuid` runtime dependency the upstream library carried; consumers needing a different format inject their own generator.